ArticleBMC infectious diseases2025
Role of biomarkers in predicting disease severity in acute dengue and SARs-CoV-2-Infected patients.
Article in BMC infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeThe early stages of both dengue infection and COVID-19 can present similarly with acute febrile illness or influenza-like symptoms, and individuals with initially mild disease may progress to more severe symptoms. We performed biomarker analysis to determine if host immune responses can predict the disease severity of both diseases.
methodsDifferential immune response profiles in patient populations were compared during acute dengue or COVID-19 using a panel of 22 soluble biomarkers. Patient plasma biomarkers were measured by ELISA or the Meso Scale Discovery platform, and statistical analysis was performed using SAS software. Receiver operating characteristic (ROC) curves were created to identify the optimal cut-off values for differentially upregulated biomarkers in severe cases. Multiple logistic regression models were developed to predict disease severity using a combination of selected biomarkers, with or without demographic data, and were analyzed using GraphPad Prism software.
resultsAlmost all of the biomarkers were higher in dengue compared to COVID-19 patients. Comparing severe to mild dengue illness, biomarkers related to monocyte activation (IL-1β, IL-12p70, soluble CD14) and Th2 cytokines (IL-4 and IL-13) were significantly elevated. Additionally, 1,3 β-D-glucan, a biomarker related to gut barrier disruption and microbial translocation, was elevated in patients with severe dengue and emerged as a key severity biomarker. In COVID-19 patients, the chemokine CXCL10 (IP-10) was the best predictive biomarker for severity. Moreover, biomarkers related to gut mucosal barrier disruption (lipopolysaccharide-binding protein, soluble CD14, and 1,3 β-D-glucan) and neutrophil extracellular trap (NET) markers were elevated in moderate to severe COVID-19. The multiple logistic regression models predicting severity for both diseases yielded ROC curves with an excellent area under the curve (AUC) greater than 0.95 and demonstrated sensitivity, specificity, positive predictive value, and negative predictive value greater than 90%.
conclusionsOur analyses indicate that gut barrier disruption and subsequent microbial translocation are common phenomena in severe cases of dengue and COVID-19. Multiple logistic regression models using a combination of specific biomarkers have the potential to improve severity prediction.
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